World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
10203
World Ranking
7110
National Ranking
3118

Overview

Olivier Pietquin is affiliated with Google in the United States and has a research focus primarily in the field of computer science. Their scholarly output spans several subfields including artificial intelligence, management science and operations research, signal processing, economics and econometrics, as well as control and systems engineering.

The scientist has contributed extensively to topics such as reinforcement learning in robotics, topic modeling, natural language processing techniques, advanced bandit algorithms research, music and audio processing, game theory and applications, and adversarial robustness in machine learning.

The following recent papers represent a selection of their published work:

  • AudioLM: A Language Modeling Approach to Audio Generation (2023), IEEE/ACM Transactions on Audio Speech and Language Processing
  • What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study (2020), arXiv (Cornell University)
  • Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision (2023), Transactions of the Association for Computational Linguistics
  • Primal Wasserstein Imitation Learning (2020), arXiv (Cornell University)
  • Munchausen Reinforcement Learning (2020), arXiv (Cornell University)

Collaborative work is a notable aspect of their research activity, with frequent coauthors including Matthieu Geist, Mathieu Laurière, Florian Strub, Julien Pérolat, and Romuald Élie.

The scientist's research has appeared often in venues such as arXiv (Cornell University), where the highest number of publications was recorded. Other frequent publication venues include the Proceedings of the AAAI Conference on Artificial Intelligence, HAL (Le Centre pour la Communication Scientifique Directe), IEEE/ACM Transactions on Audio Speech and Language Processing, and Transactions of the Association for Computational Linguistics.

Best Publications

  • Noisy Networks For Exploration

    Meire Fortunato;Mohammad Gheshlaghi Azar;Bilal Piot;Jacob Menick

  • Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

    Matej Vecerík;Todd Hester;Jonathan Scholz;Fumin Wang

  • Deep Q-learning from Demonstrations

    Todd Hester;Matej Vecerik;Olivier Pietquin;Marc Lanctot

  • Deep Q-learning From Demonstrations.

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • AudioLM: A Language Modeling Approach to Audio Generation

    Unknown

  • Modulating early visual processing by language

    Harm de Vries;Florian Strub;Jeremie Mary;Hugo Larochelle

  • GuessWhat?! Visual Object Discovery through Multi-modal Dialogue

    Harm de Vries;Florian Strub;Sarath Chandar;Olivier Pietquin

  • A probabilistic framework for dialog simulation and optimal strategy learning

    O. Pietquin;T. Dutoit

  • Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation

    Alexandre Bérard;Olivier Pietquin;Laurent Besacier;Christophe Servan

  • End-to-End Automatic Speech Translation of Audiobooks

    Alexandre Berard;Laurent Besacier;Ali Can Kocabiyikoglu;Olivier Pietquin

  • Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation

    Alexandre Berard;Olivier Pietquin;Christophe Servan;Laurent Besacier

  • Learning from Demonstrations for Real World Reinforcement Learning

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • Machine learning for spoken dialogue systems

    Oliver Lemon;Olivier Pietquin

  • A survey on metrics for the evaluation of user simulations

    Olivier Pietquin;Helen F. Hastie

  • Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision

    Unknown

  • Observe and Look Further: Achieving Consistent Performance on Atari

    Tobias Pohlen;Bilal Piot;Todd Hester;Mohammad Gheshlaghi Azar

  • End-to-end optimization of goal-driven and visually grounded dialogue systems

    Florian Strub;Harm de Vries;Jérémie Mary;Bilal Piot

  • Sample-efficient batch reinforcement learning for dialogue management optimization

    Olivier Pietquin;Matthieu Geist;Senthilkumar Chandramohan;Hervé Frezza-Buet

  • User Simulation in Dialogue Systems Using Inverse Reinforcement Learning.

    Senthilkumar Chandramohan;Matthieu Geist;Fabrice Lefèvre;Olivier Pietquin

  • Kalman temporal differences

    Matthieu Geist;Olivier Pietquin

  • Inverse Reinforcement Learning through Structured Classification

    Edouard Klein;Matthieu Geist;Bilal Piot;Olivier Pietquin

  • End-to-end optimization of goal-driven and visually grounded dialogue systems Harm de Vries

    Florian Strub;Harm de Vries;Jeremie Mary;Bilal Piot

Frequent Co-Authors

Aaron Courville
Aaron Courville University of Montreal
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Laurent Besacier
Laurent Besacier Grenoble Alpes University
Oliver Lemon
Oliver Lemon Heriot-Watt University
Thierry Dutoit
Thierry Dutoit University of Mons
Hugo Larochelle
Hugo Larochelle Google (United States)
Steve Young
Steve Young University of Cambridge
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Marc Lanctot
Marc Lanctot DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)

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